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Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images

International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.

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Bibliographic Details
Main Authors: Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr.
Other Authors: khusairi@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Mycobacterium tuberculosis
Zeihl-Neelsen stained tissue images
Tuberculosis (TB)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21497
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spelling my.unimap-214972012-10-21T08:32:00Z Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. khusairi@ppinang.uitm.edu.my Mycobacterium tuberculosis Zeihl-Neelsen stained tissue images Tuberculosis (TB) International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia. Automatic detection of Mycobacterium tuberculosis improves accuracy, sensitivity and efficiency of diagnosis compared to manual method. However, the process is difficult, especially in Zeihl-Neelsen stained tissue images due to intensity inhomogeneity and tissue background complexity. In this paper, an automated approach to segment Mycobacterium tuberculosis from tissue slide images using fuzzy c-mean clustering procedure is proposed. The procedure provides a basic step for detecting the presence of tuberculosis bacilli. First, initial filter is used to assist the clustering process by removing the tissues images which remain blue after counterstaining process. Then, fuzzy c-mean clustering is applied to segment the bacilli. Three colour models, RGB, HSI and C-Y are analysed to identify the colour model that perform significant segmentation performance. Finally, a 5×5 median filter and region growing was used to eliminate small regions and noises. The proposed methods have been analysed for several TB slide images under various conditions. The results indicated that fuzzy c-mean clustering using saturation component of C-Y colour model has achieved the best segmentation result with an accuracy of 99.54%. 2012-10-21T08:31:59Z 2012-10-21T08:31:59Z 2010-10-16 Working Paper 978-967-5760-03-7 http://hdl.handle.net/123456789/21497 en Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) Universiti Malaysia Perlis (UniMAP) Centre for Graduate Studies
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Mycobacterium tuberculosis
Zeihl-Neelsen stained tissue images
Tuberculosis (TB)
spellingShingle Mycobacterium tuberculosis
Zeihl-Neelsen stained tissue images
Tuberculosis (TB)
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
description International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.
author2 khusairi@ppinang.uitm.edu.my
author_facet khusairi@ppinang.uitm.edu.my
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
format Working Paper
author Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
author_sort Muhammad Khusairi, Osman
title Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_short Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_full Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_fullStr Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_full_unstemmed Application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue images
title_sort application of fuzzy c-mean clustering technique for mycobacterium tuberculosis detection in ziehl-neelsen stained tissue images
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21497
_version_ 1643793408697827328
score 13.214268

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